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020			_a9780387699424 _99780387699424
024	7		_a10.1007/9780387699424 _2doi
035			_avtls000332047
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050		4	_aQA76.575
100	1		_aGong, Yihong. _eautor _9304588
245	1	0	_aMachine Learning for Multimedia Content Analysis / _cby Yihong Gong, Wei Xu.
264		1	_aBoston, MA : _bSpringer US, _c2007.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aUnsupervised Learning -- Dimension Reduction -- Data Clustering Techniques -- Generative Graphical Models -- of Graphical Models -- Markov Chains and Monte Carlo Simulation -- Markov Random Fields and Gibbs Sampling -- Hidden Markov Models -- Inference and Learning for General Graphical Models -- Discriminative Graphical Models -- Maximum Entropy Model and Conditional Random Field -- Max-Margin Classifications.
520			_aChallenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly. Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons. Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aXu, Wei. _eautor _9304589
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387699387
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-69942-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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